AI Architect

Derex Technologies Inc

. Architect, design, and guide implementation of enterprise-grade AI and ML solutions .

Posted 10/30/2026full-timeRemote • CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing enterprise-grade AI and ML solutions, with a strong focus on Generative AI and LLM frameworks. Proficient in Python programming, AI/ML frameworks, and cloud platforms, while effectively collaborating with cross-functional teams and mentoring junior engineers.

Highest-signal resume keywords

Python ProgrammingAI/ML FrameworksGenerative AI ArchitecturesCloud & MLOps PlatformsTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Coding AssistantsTensorFlowPyTorchKerasAWS SageMakerAzure Machine LearningGoogle AI PlatformModel DeploymentFeature EngineeringPrompt Engineering

Soft Skills

Analytical SkillsProblem-Solving SkillsCommunication SkillsMentoring Skills

Tools & Technologies

Visual Studio CodeIntelliJ IDEAEclipseAzure Cosmos DBAzure AI SearchLangChainLlamaIndexSemantic KernelAutoGenGitHub Actions

Industry Keywords

Generative AIAI GovernanceResponsible AICompliance ControlsArchitectural Standards

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaJavaScriptKerasPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Architect, design, and guide implementation of enterprise-grade AI and ML solutions
Collaborate with data scientists, ML engineers, platform engineers, and software teams
Provide hands-on technical leadership in Python and ML, GenAI, and LLM frameworks
Oversee data preparation, feature engineering, vectorization, and model development
Evaluate emerging AI, ML, LLM, and agentic-system advancements and incorporate relevant innovations into roadmaps
Optimize AI and Generative AI models for performance, latency, scalability, observability, and cost efficiency
Conduct technical design reviews, code reviews, and architecture assessments
Diagnose and resolve complex AI system issues
Design enterprise capabilities using AI coding assistants, copilots, and developer productivity tools
Lead extensions, plugins, and IDE integrations for Visual Studio Code, IntelliJ IDEA, and Eclipse
Apply prompt engineering, LLM behavior, context-window optimization, fine-tuning, and agentic workflows
Design, train, and evaluate custom generative models
Architect data acquisition, cleaning, text chunking, RAG, and vector-store ingestion pipelines
Integrate enterprise solutions with OpenAI, Azure OpenAI, LangChain, and other orchestration frameworks
Design and maintain the LLM data layer using Azure Cosmos DB, Azure AI Search, vector stores, and embedding indexes
Develop agentic automation using LangChain, LlamaIndex, Semantic Kernel, and AutoGen
Apply DevOps practices using CI/CD, GitHub Actions, Azure DevOps, and modern testing frameworks
Lead copilot initiatives across application development, customer support, operations, and productivity automation
Design, test, and refine prompts, prompt templates, and generative AI interaction models
Align prompt behavior with business outcomes, brand voice, and user experience requirements
Build and maintain an enterprise prompt library with versioning, governance, reuse, and workflow integration
Participate in fine-tuning and reinforcement learning processes
Diagnose and refine suboptimal prompts to ensure predictable and aligned LLM behavior

Requirements

What you’ll need

Strong hands-on programming expertise in at least one modern language such as Python, C#, Java, or JavaScript
Deep proficiency with AI/ML frameworks and libraries including TensorFlow, PyTorch, and Keras
Advanced understanding of Generative AI architectures, including GANs, VAEs, and transformer-based models
Practical experience using AI Coding Assistants
Hands-on expertise with Agentic AI platforms, including Microsoft Copilot Agents, AutoGPT, CrewAI, Anthropic Claude, and other autonomous workflow systems
Strong command of Cloud & MLOps platforms, such as AWS SageMaker, Azure Machine Learning, and Google AI Platform
Experience building automated model deployment, monitoring, and retraining pipelines
Solid experience architecting solutions on AWS, Azure, and GCP
Proven ability to mentor and guide junior and mid-level AI/ML engineers
Ability to conduct AI/ML and Generative AI code reviews
Strong analytical and problem-solving skills
Excellent communication skills for technical and business stakeholders
Experience defining enterprise AI architectures, reference models, reusable design patterns, and architectural standards
Ability to define and implement AI governance frameworks, Responsible AI guidelines, compliance controls, model guardrails, safety protocols, and risk-mitigation practices

Benefits

Comp & perks

Employees can work remotely